In [1]:
!pip install yfinance==0.2.4
#!pip install pandas==1.3.3

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In [3]:
import yfinance as yf
import pandas as pd
In [5]:
tesla = yf.Ticker("TSLA")
In [6]:
tesla_data = tesla.history(period="max")
In [7]:
tesla_data.reset_index(inplace=True)
tesla_data.head()
Out[7]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 00:00:00-04:00 1.266667 1.666667 1.169333 1.592667 281494500 0.0 0.0
1 2010-06-30 00:00:00-04:00 1.719333 2.028000 1.553333 1.588667 257806500 0.0 0.0
2 2010-07-01 00:00:00-04:00 1.666667 1.728000 1.351333 1.464000 123282000 0.0 0.0
3 2010-07-02 00:00:00-04:00 1.533333 1.540000 1.247333 1.280000 77097000 0.0 0.0
4 2010-07-06 00:00:00-04:00 1.333333 1.333333 1.055333 1.074000 103003500 0.0 0.0
In [8]:
tesla_data.tail()
Out[8]:
Date Open High Low Close Volume Dividends Stock Splits
3216 2023-04-10 00:00:00-04:00 179.940002 185.100006 176.110001 184.509995 142154600 0.0 0.0
3217 2023-04-11 00:00:00-04:00 186.690002 189.190002 185.649994 186.789993 115770900 0.0 0.0
3218 2023-04-12 00:00:00-04:00 190.740005 191.580002 180.309998 180.539993 150256300 0.0 0.0
3219 2023-04-13 00:00:00-04:00 182.960007 186.500000 180.940002 185.899994 112933000 0.0 0.0
3220 2023-04-14 00:00:00-04:00 183.949997 186.279999 182.009995 185.000000 96306500 0.0 0.0
In [9]:
gme = yf.Ticker("GME")
In [10]:
gme_data = gme.history(period="max")
In [11]:
gme_data.head()
Out[11]:
Open High Low Close Volume Dividends Stock Splits
Date
2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691666 76216000 0.0 0.0
2002-02-14 00:00:00-05:00 1.712708 1.716074 1.670627 1.683251 11021600 0.0 0.0
2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658002 1.674834 8389600 0.0 0.0
2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0
In [13]:
import pandas as pd
import requests
from bs4 import BeautifulSoup
In [14]:
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"
data = requests.get(url).text
In [16]:
soup = BeautifulSoup(data, 'html5lib')
In [24]:
gme_revenue = pd.DataFrame(columns=['Date', 'Revenue'])

for table in soup.find_all('table'):

    if ('GameStop Quarterly Revenue' in table.find('th').text):
        rows = table.find_all('tr')
        
        for row in rows:
            col = row.find_all('td')
            
            if col != []:
                date = col[0].text
                revenue = col[1].text.replace(',','').replace('$','')

                gme_revenue = gme_revenue.append({"Date":date, "Revenue":revenue}, ignore_index=True)
In [26]:
gme_revenue.tail()
Out[26]:
Date Revenue
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709
In [27]:
 

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
/tmp/ipykernel_2219/1765937996.py in <module>
----> 1 make_graph(tesla_data, tesla_revenue, "Tesla")

NameError: name 'make_graph' is not defined
In [28]:
!pip install yfinance==0.1.67
#!pip install pandas==1.3.3
#!pip install requests==2.26.0
!mamba install bs4==4.10.0 -y
#!pip install plotly==5.3.1
!pip install html5lib
!pip install --upgrade nbformat

Collecting yfinance==0.1.67
  Downloading yfinance-0.1.67-py2.py3-none-any.whl (25 kB)
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Installing collected packages: yfinance
  Attempting uninstall: yfinance
    Found existing installation: yfinance 0.2.4
    Uninstalling yfinance-0.2.4:
      Successfully uninstalled yfinance-0.2.4
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Looking for: ['bs4==4.10.0']

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  - python 3.7.*


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In [30]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [32]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()
In [35]:
tesla_revenue = pd.DataFrame(columns=['Date', 'Revenue'])

for table in soup.find_all('table'):

    if ('Tesla Quarterly Revenue' in table.find('th').text):
        rows = table.find_all('tr')
        
        for row in rows:
            col = row.find_all('td')
            
            if col != []:
                date = col[0].text
                revenue = col[1].text.replace(',','').replace('$','')

                tesla_revenue = tesla_revenue.append({"Date":date, "Revenue":revenue}, ignore_index=True)
In [36]:
make_graph(tesla_data, tesla_revenue, "Tesla")
In [41]:
gme_revenue = pd.DataFrame(columns = ['Date', 'Revenue'])

for row in soup.find_all("tbody")[1].find_all("tr"):
    col = row.find_all("td")
    Date = col[0].text
    revenue = col[1].text.replace("$", "").replace(",", "")
    
    gme_revenue = gme_revenue.append({"Date": Date, "Revenue": revenue}, ignore_index = True)
In [46]:
GameStop = yf.Ticker("GME")

gme_data = GameStop.history(period = 'max')
gme_data.reset_index(inplace = True)
gme_data.head()
Out[46]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 00:00:00-05:00 1.620128 1.693350 1.603296 1.691666 76216000 0.0 0.0
1 2002-02-14 00:00:00-05:00 1.712707 1.716073 1.670626 1.683250 11021600 0.0 0.0
2 2002-02-15 00:00:00-05:00 1.683250 1.687458 1.658001 1.674834 8389600 0.0 0.0
3 2002-02-19 00:00:00-05:00 1.666418 1.666418 1.578048 1.607505 7410400 0.0 0.0
4 2002-02-20 00:00:00-05:00 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0
In [47]:
make_graph(gme_data, gme_revenue, 'GameStop')
In [ ]:
In [ ]: